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      MiR-93-5p Promotes Cell Proliferation through Down-Regulating PPARGC1A in Hepatocellular Carcinoma Cells by Bioinformatics Analysis and Experimental Verification

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          Abstract

          Peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PPARGC1A, formerly known as PGC-1a) is a transcriptional coactivator and metabolic regulator. Previous studies are mainly focused on the association between PPARGC1A and hepatoma. However, the regulatory mechanism remains unknown. A microRNA associated with cancer (oncomiR), miR-93-5p, has recently been found to play an essential role in tumorigenesis and progression of various carcinomas, including liver cancer. Therefore, this paper aims to explore the regulatory mechanism underlying these two proteins in hepatoma cells. Firstly, an integrative analysis was performed with miRNA–mRNA modules on microarray and The Cancer Genome Atlas (TCGA) data and obtained the core regulatory network and miR-93-5p/ PPARGC1A pair. Then, a series of experiments were conducted in hepatoma cells with the results including miR-93-5p upregulated and promoted cell proliferation. Thirdly, the inverse correlation between miR-93-5p and PPARGC1A expression was validated. Finally, we inferred that miR-93-5p plays an essential role in inhibiting PPARGC1A expression by directly targeting the 3′-untranslated region (UTR) of its mRNA. In conclusion, these results suggested that miR-93-5p overexpression contributes to hepatoma development by inhibiting PPARGC1A. It is anticipated to be a promising therapeutic strategy for patients with liver cancer in the future.

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          Most cited references37

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          The Warburg effect in tumor progression: mitochondrial oxidative metabolism as an anti-metastasis mechanism.

          Compared to normal cells, cancer cells strongly upregulate glucose uptake and glycolysis to give rise to increased yield of intermediate glycolytic metabolites and the end product pyruvate. Moreover, glycolysis is uncoupled from the mitochondrial tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS) in cancer cells. Consequently, the majority of glycolysis-derived pyruvate is diverted to lactate fermentation and kept away from mitochondrial oxidative metabolism. This metabolic phenotype is known as the Warburg effect. While it has become widely accepted that the glycolytic intermediates provide essential anabolic support for cell proliferation and tumor growth, it remains largely elusive whether and how the Warburg metabolic phenotype may play a role in tumor progression. We hereby review the cause and consequence of the restrained oxidative metabolism, in particular in the context of tumor metastasis. Cells change or lose their extracellular matrix during the metastatic process. Inadequate/inappropriate matrix attachment generates reactive oxygen species (ROS) and causes a specific type of cell death, termed anoikis, in normal cells. Although anoikis is a barrier to metastasis, cancer cells have often acquired elevated threshold for anoikis and hence heightened metastatic potential. As ROS are inherent byproducts of oxidative metabolism, forced stimulation of glucose oxidation in cancer cells raises oxidative stress and restores cells' sensitivity to anoikis. Therefore, by limiting the pyruvate flux into mitochondrial oxidative metabolism, the Warburg effect enables cancer cells to avoid excess ROS generation from mitochondrial respiration and thus gain increased anoikis resistance and survival advantage for metastasis. Consistent with this notion, pro-metastatic transcription factors HIF and Snail attenuate oxidative metabolism, whereas tumor suppressor p53 and metastasis suppressor KISS1 promote mitochondrial oxidation. Collectively, these findings reveal mitochondrial oxidative metabolism as a critical suppressor of metastasis and justify metabolic therapies for potential prevention/intervention of tumor metastasis.
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            Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks.

            MicroRNAs (miRNA) play critical roles in regulating gene expressions at the posttranscriptional levels. The prediction of disease-related miRNA is vital to the further investigation of miRNA's involvement in the pathogenesis of disease. In previous years, biological experimentation is the main method used to identify whether miRNA was associated with a given disease. With increasing biological information and the appearance of new miRNAs every year, experimental identification of disease-related miRNAs poses considerable difficulties (e.g. time-consumption and high cost). Because of the limitations of experimental methods in determining the relationship between miRNAs and diseases, computational methods have been proposed. A key to predict potential disease-related miRNA based on networks is the calculation of similarity among diseases and miRNA over the networks. Different strategies lead to different results. In this review, we summarize the existing computational approaches and present the confronted difficulties that help understand the research status. We also discuss the principles, efficiency and differences among these methods. The comprehensive comparison and discussion elucidated in this work provide constructive insights into the matter.
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              Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources.

              Since the discovery of the regulatory function of microRNA (miRNA), increased attention has focused on identifying the relationship between miRNA and disease. It has been suggested that computational method are an efficient way to identify potential disease-related miRNAs for further confirmation using biological experiments. In this paper, we first highlighted three limitations commonly associated with previous computational methods. To resolve these limitations, we established disease similarity subnetwork and miRNA similarity subnetwork by integrating multiple data sources, where the disease similarity is composed of disease semantic similarity and disease functional similarity, and the miRNA similarity is calculated using the miRNA-target gene and miRNA-lncRNA (long non-coding RNA) associations. Then, a heterogeneous network was constructed by connecting the disease similarity subnetwork and the miRNA similarity subnetwork using the known miRNA-disease associations. We extended random walk with restart to predict miRNA-disease associations in the heterogeneous network. The leave-one-out cross-validation achieved an average area under the curve (AUC) of 0:8049 across 341 diseases and 476 miRNAs. For five-fold cross-validation, our method achieved an AUC from 0:7970 to 0:9249 for 15 human diseases. Case studies further demonstrated the feasibility of our method to discover potential miRNA-disease associations. An online service for prediction is freely available at http://ifmda.aliapp.com.
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                Author and article information

                Journal
                Genes (Basel)
                Genes (Basel)
                genes
                Genes
                MDPI
                2073-4425
                22 January 2018
                January 2018
                : 9
                : 1
                : 51
                Affiliations
                [1 ]State Key Laboratory for Medical Genomics, Shanghai Institute of Hematology, Rui Jin Hospital Affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China; wanxiruqiqi@ 123456sina.com
                [2 ]Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; andy7142003@ 123456163.com (Z.B.); hyhyj2002@ 123456163.com (Y.H.); duanjuan200609@ 123456163.com (J.D.)
                [3 ]Department of Clinical Laboratory, Jinjiang Municipal Hospital, Jinjiang 362200, China
                [4 ]School of Computer Science and Technology, Tianjin University, Tianjin 300350, China
                Author notes
                [* ]Correspondence: liaozj100@ 123456163.com (Z.L.); weileyi@ 123456tju.edu.cn (L.W.)
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-1444-190X
                Article
                genes-09-00051
                10.3390/genes9010051
                5793202
                29361788
                3aa0664f-5fb4-426c-a99d-a148d695cc08
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 07 December 2017
                : 16 January 2018
                Categories
                Article

                mir-93-5p,ppargc1a,proliferation,hepatoma,mirna-mrna module
                mir-93-5p, ppargc1a, proliferation, hepatoma, mirna-mrna module

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